Radio frequency signal analysis and classification using time-frequency information

ABSTRACT

A signal analysis mechanism for processing and classifying an RF signal received at a WLAN device. A plurality of spectral data captures comprising time-frequency data and receive signal strength indicator (RSSI) data associated with the RF signal received at the WLAN device are generated. The time-frequency and RSSI data of each of the plurality of spectral data captures is processed to determine whether each spectral data capture is a narrowband signal capture. If the plurality of spectral data captures are narrowband signal captures, a narrowband interference signal type associated with the received RF signal is determined based, at least in part, on a plurality of parameters associated with the narrowband signal captures.

RELATED APPLICATION

This application claims the priority benefit of U.S. ProvisionalApplication Ser. No. 61/060,628 filed Jun. 11, 2008.

BACKGROUND

Embodiments of the inventive subject matter generally relate to thefield of wireless communication systems, and, more particularly, totechniques for processing and classifying radio frequency (RF) signals.

Wireless local area (WLAN) devices share the unlicensed frequencyspectrum with various commercial devices, such as microwave ovens,cordless phones, baby monitors, and devices based on various otherwireless standards, such as wireless USB, Zigbee®, Bluetooth® andHomeRF. The RF signals emitted by these devices act as interference toWLAN devices and therefore may impact the performance of the WLANdevices.

SUMMARY

Various embodiments are disclosed of an apparatus and method forprocessing and classifying an RF signal received at a WLAN device. Inone embodiment, a plurality of spectral data captures comprisingtime-frequency data and receive signal strength indicator (RSSI) dataassociated with the RF signal received at the WLAN device are generated.The time-frequency and RSSI data of each of the plurality of spectraldata captures is processed to determine whether each spectral datacapture is a narrowband signal capture. If the plurality of spectraldata captures are narrowband signal captures, a narrowband signal typeassociated with the received RF signal is determined based, at least inpart, on a plurality of parameters associated with the narrowband signalcaptures.

BRIEF DESCRIPTION OF THE DRAWINGS

The present embodiments may be better understood, and numerous objects,features, and advantages made apparent to those skilled in the art byreferencing the accompanying drawings.

FIG. 1 is an example block diagram of a receiver unit of a WLAN deviceconfigured to determine whether received RF signals are interferencesignals and determine the type of interference signals;

FIG. 2 is an example flow diagram of a method for processing andclassifying narrowband interference signals in a WLAN device;

FIG. 3 is a diagram illustrating a technique for determining whether anFFT capture is a narrowband FFT capture;

FIG. 4 is a conceptual diagram of an example wireless communicationsystem;

FIG. 5 is an example flow diagram of a method for processing andclassifying narrowband interference signals in a wireless communicationsystem; and

FIG. 6 is an example block diagram of a wireless device.

DESCRIPTION OF EMBODIMENT(S)

The description that follows includes exemplary systems, methods,techniques, instruction sequences and computer program products thatembody techniques of the present inventive subject matter. However, itis understood that the described embodiments may be practiced withoutthese specific details. In other instances, well-known instructioninstances, protocols, structures and techniques have not been shown indetail in order not to obfuscate the description.

In various embodiments, a signal analysis mechanism detects andclassifies RF signals that interfere with WLAN devices using theirtemporal and frequency characteristics. The temporal and frequencycharacteristics are obtained from the RF signal observed at the antennaof the WLAN device. In one implementation, the signal extraction isachieved by collecting spectral scan data (e.g., time-frequency data)produced by a spectral scan unit (e.g., an FFT engine) on the receiverof the WLAN device. A signal classification unit processes the spectralscan data to identify the interference signal and analyzes other signalparameters to classify the interference signal as a certain interferencesignal type, as will be further described below.

FIG. 1 is an example block diagram of a receiver unit 100 configured todetermine whether received RF signals are interference signals anddetermine the type of interference signals. The receiver unit 100 may beimplemented in a transceiver of a WLAN device (e.g., as illustrated inFIG. 4). In various implementations, the receiver unit 100 includes anantenna 101, an analog front end (AFE) 105, a Fast Fourier Transform(FFT) unit 110, a packet detection unit 120, a frequency offsetestimation unit 130, a channel estimation unit 140, a signalclassification unit 150, and a spectral scan controller 155.

During operation, the antenna 101 can receive RF signals and provide thereceived RF signals to the analog front end 105. The analog front end105 can include 1) one or more amplifying stages to amplify the receivedRF signal, 2) one or more filtering stages to remove unwanted bands offrequencies, 3) mixer stages to down-convert the received RF signal, 4)an automatic gain control (AGC) unit to adjust the gain to anappropriate level for a range of received signal amplitude levels, 5) ananalog to digital converter (ADC) to convert the received RF signal intoa digital signal, etc. The FFT unit 110 generates FFT data, e.g.,time-frequency data associated with the digital representation of thereceived RF signal. When the WLAN device of the receiver unit 100 isperforming WLAN operations (i.e., processing WLAN traffic), the FFT unit110 provides the FFT data to the packet detection unit 120. The packetdetection unit 120 may use information in the received signal's preambleto detect an incoming packet. For example, the packet detection unit 120can perform self-correlation on the received signal, use a combinationof cross correlation (with a known STF symbol) and self-correlation, oruse any suitable method to detect the packet.

The frequency offset estimation unit 130 can use the short trainingfields (STFs) and/or the long training fields (LTFs) to estimate acarrier frequency offset in the received RF signal. The carrierfrequency offset in the received RF signal may then be corrected usingthe carrier frequency offset estimate. The carrier frequency offset maybe caused by improper synchronization between the crystal frequencygenerator on the receiver and transmitter. The channel estimation unit140 can use the LTFs to determine channel estimates (e.g., estimatechannel coefficients and channel response). The channel estimates may bedetermined to counter the effect of the communication channel oninformation symbols in the received RF signal. Channel estimatesdescribe the effect (attenuation, distortion, etc.) of the communicationchannel on signals that pass through it. From the channel estimationunit 140, other digital processing units can use the channel estimates,further process the received symbols, and retrieve one or moreinformation symbols. In one embodiment, subsequent digital processingblocks can include a demodulator, a deinterleaver, a decoder, and otherdigital components depending on the encoding applied beforetransmission.

In various implementations, when the WLAN device of the receiver unit100 is not processing WLAN traffic (i.e., transmitting or receiving WLANsignals), the receiver unit 100 may initiate spectral scan operations toprocess a received RF signal and determine whether the RF signal is aninterference signal and the type of interference signal. The FFT unit110 generates a plurality of FFT captures comprising time-frequency dataand receive signal strength indicator (RSSI) data associated with thereceived RF signal and provides the FFT captures to the classificationunit 150, as noted in stage A. The classification unit 150 processes thetime-frequency and RSSI data of each of the FFT captures to determinewhether each FFT capture is a narrowband signal capture, as noted instage B. If the FFT captures are narrowband signal captures, at stage C,the classification unit 150 determines a narrowband signal typeassociated with the received RF signal based, at least in part, on aplurality of parameters associated with the narrowband signal captures,as will be further described below with reference to FIG. 2. In oneexample, duration, burst width, and frequency parameters of narrowbandsignal components may be used to classify the received narrowbandsignals. For instance, the received narrowband signals may be classifiedas Bluetooth® signals, microwave oven leakage signals, frequency-hoppingspread-spectrum (FHSS) cordless phones, baby monitors, etc. In someimplementations, the classification unit 150 may also determine whetherthe received RF signals are wideband signals and identify the widebandsignal type (e.g., WLAN signals, direct-sequence spread-spectrum (DSSS)cordless phones, etc.). After determining the type of RF interferencesignal, the classification unit 150 provides the RF signal typeclassification information to the spectral scan controller 155 foranalysis and to determine whether to take any corrective action (e.g.,switch frequency channels on the WLAN device).

In one embodiment, the spectral scan controller 155 enables and disablesthe FFT spectral scan operations for signal analysis. The spectral scancontroller 155 may be implemented as an intrusive, high priority task.In this example, the spectral scan operations may have higher prioritythan data traffic and therefore the data traffic may be suspended untilthe spectral scan operations have been completed. In another example,the FFT spectral scan operations can be implemented as a backgroundprocess for non-intrusive operation. In this example, the spectral scanoperations are low priority compared to data traffic and therefore thespectral scan operations co-exist with the data traffic. It is noted,however, that in some implementations the FFT spectral scan operationsmay be implemented as low priority operations in some cases and highpriority operations in other cases.

In some embodiments, the FFT unit 110 may be implemented in hardware andthe classification unit 150 and spectral scan controller 155 may beimplemented in software. It is noted, however, that each of thecomponents of the receiver unit 110 (e.g., the FFT unit 110, theclassification unit 150, etc.) may be implemented in software and/orhardware.

It should be noted that the components described with reference to FIG.1 are meant to be exemplary only, and are not intended to limit theinventive subject matter to any specific set of components orconfigurations. For example, in various embodiments, one or more of thecomponents described may be omitted, combined, modified, or additionalcomponents included, as desired. For instance, in some embodiments, thespectral scan operations for analysis and classification of RF signalsdescribed herein may be implemented within various other types ofwireless devices.

FIG. 2 is an example flow diagram of a method for processing andclassifying narrowband interference signals in a WLAN device. Thenarrowband signal detection algorithm uses the bursty nature ofnarrowband signals to identify the signals, and uses the properties ofthe narrowband bursts to classify each of the signals as a certainnarrowband signal type. At block 210, an FFT capture associated with anRF signal acquired by the receiver unit 100 is received at theclassification unit 150 from the FFT unit 110. The FFT capture includestime-frequency data and RSSI data associated with the received RFsignal. The FFT unit 110 may provide various FFT captures associatedwith the received RF signal to the classification unit 150 in order toclassify the received RF signal. From block 210, the flow continues atblock 215.

At block 215, it is determined whether a timestep is greater than apredetermined timestep threshold. The timestep can be defined as thetime difference between the previous FFT capture and the current FFTcapture. In one example, the classification unit 150 may calculate thetimestep and determine whether the timestep is greater than thepredetermined timestep threshold. The timestep may help detect when asignificant amount of time elapses between FFT captures. For example, ifthe WLAN device begins to process WLAN traffic, the FFT signal analysisoperations may be interrupted for a considerable amount of time. If theclassification unit 150 detects that too much time elapsed between thecurrent FFT capture and the previous FFT capture, i.e., if the timestepis greater than the predetermine timestep threshold, the FFT capturesfor the current narrowband burst analysis are stopped and the flowcontinues at block 255, where the width of the current narrowband burstis calculated. In some examples, before analyzing the timestep, theclassification unit 150 may first determine whether the current FFTcapture is the first detected FFT capture. For example, theclassification unit 150 may read a counter that keeps track of thenumber of FFT captures that have been received during the current signalanalysis operation. If the current FFT capture is the first detected FFTcapture, the classification unit 150 may skip the timestep calculationsof block 215 and the flow continues to block 220. Also, if the timestepis less than the predetermine timestep threshold, the flow continues toblock 220.

At block 220, it is determined whether the RSSI data included in thereceived FFT capture is greater than a predetermined RSSI threshold. Inone example, the classification unit 150 determines whether the RSSIdata is greater than the predetermined RSSI threshold. From block 220,if the RSSI data is greater the predetermined RSSI threshold, the flowcontinues to block 225. If the RSSI is less than the predetermined RSSIthreshold, the flow continues to block 222.

At block 225, if the RSSI data is greater the predetermined RSSIthreshold, it is determined whether the FFT capture associated with thereceived RF signal is a narrowband signal capture. For example, theclassification unit 150 determines whether the received FFT capture is anarrowband signal capture or a wideband signal capture. As noted above,the received FFT capture comprises spectral data including a pluralityof FFT frequency bins. In one implementation, the classification unit150 counts the number of frequency bins (i.e., the bin count) associatedwith the received FFT capture that are above a predetermined decibelthreshold. As illustrated in FIG. 3, the predetermined decibel thresholdmay be selected to be X decibels below a maximum decibel value (i.e.,peak) of the FFT capture. For example, if the maximum decibel value orpeak of the received FFT capture is at 10 dB and X is equal to 4 dB,then the threshold is set at 6 dB. The classification unit 150 thencompares the bin count to a predetermined bin count threshold todetermine whether the received FFT capture is narrowband or wideband.

If the bin count is greater than or equal to the predetermined bin countthreshold, then the received FFT capture is classified as a widebandsignal capture. If the bin count is less than the predetermined bincount threshold, then the received FFT capture is classified as anarrowband signal capture. From block 225, if the received FFT captureis classified as a wideband signal capture, the flow continues to block230. At block 230, the classification unit 150 provides an indication tothe spectral scan controller 155 that the received FFT capture was awideband signal capture. If the received FFT capture is classified as anarrowband signal capture, the flow continues to block 235.

At block 235, it is determined whether the received FFT capture is thefirst narrowband FFT capture that has been received during a signalanalysis operation. For example, the classification unit 150 may read acounter that keeps track of the number of narrowband FFT captures thathave been received (i.e., the narrowband capture count) during thesignal analysis operation associated with a narrowband burst. If thecounter has a value of zero, then the received FFT capture is the firstFFT capture, and the flow continues to block 240. If the counter has avalue other than zero, then the received FFT capture is not the firstFFT capture, and the flow continues to block 245.

At block 240, if the received FFT capture is the first FFT capture, astart time of the current narrowband burst is recorded. For example, theclassification unit 150 may record the start time of the currentnarrowband burst in a register or memory. The start time will be used,along with a subsequently recorded stop time, to determine the width ofthe current narrowband burst. After recording the start time, or if itis determined that the received FFT capture is not the first FFTcapture, the flow continues to block 245.

At block 245, the narrowband capture count is incremented, a timestampassociated with the received FFT capture is stored, and the RSSI andfrequency associated with the received FFT capture is recorded. Forexample, the classification unit 150 may update the counter keepingtrack of the narrowband capture count, and record the timestamp, theRSSI and frequency associated with the received FFT capture. Thenarrowband capture count and the timestamp may help determine when anarrowband burst has been detected. From block 245, the flow continuesto block 250.

At block 250, it is determined whether a narrowband burst has beendetected. In one implementation, the classification unit 150 may readthe narrowband capture count to determine whether a certain number ofconsecutive narrowband captures have been received. Additionally, theclassification unit 150 may calculate the duration between the first andlast received narrowband captures and determine whether the duration isgreater than a predetermined duration threshold. In this implementation,a narrowband burst is considered detected when the narrowband capturecount is above a predetermined count threshold and when the duration isabove the predetermined duration threshold. In one example, the durationbetween the first and last received narrowband captures may becalculated by finding the difference between the timestamp of the lastreceived narrowband capture and the timestamp of the first receivednarrowband capture. If a narrowband burst is detected, the flowcontinues to block 255. If a narrowband burst is not detected, the flowloops to block 205, where the classification unit 150 receives asubsequent FFT capture.

Similarly, at block 222, if the RSSI data is less than the predeterminedthreshold (block 220), it is determined whether a narrowband burst hasbeen detected. For example, the classification unit may determinewhether a narrowband burst has been detected using a similar process asdescribed above with reference to block 250. In this example, theclassification unit 150 may use the timestamp from the previousnarrowband capture to calculate the duration of the burst and determinewhether the duration is greater than the predetermined threshold. Also,the classification unit 150 may determine whether the narrowband capturecount (which at this point has been updated to account for the previousnarrowband capture) is greater than the count threshold. If a narrowbandburst is not detected, then the flow ends. If a narrowband burst isdetected, the flow continues to block 255.

At block 255, a stop time of the current narrowband burst is recordedand the width of the current narrowband burst is calculated. Forexample, the classification unit 150 records the stop time of thecurrent narrowband burst, and determines the width of the currentnarrowband burst by finding the difference between the recorded stoptime and the recorded start time of the current narrowband burst. Insome implementations, besides the start time, the stop time, and thewidth of the detected narrowband burst, the classification unit 150 mayalso calculate the average frequency of the FFT bins with maximum powerassociated with the narrowband FFT captures of the detected narrowbandburst. In addition, the classification unit 150 may calculate an averageRSSI value from the recorded RSSI data of the narrowband FFT captures.Furthermore, the classification unit 150 may calculate a spread ofmaximum bin index based on the frequency difference between thenarrowband FFT capture whose maximum bin value is at the lowestfrequency and the narrowband FFT capture whose maximum bin value is atthe highest frequency for the detected narrowband burst. In someimplementations, the classification unit 150 may use this information,in addition to the narrowband capture count and duration information, tofurther certify that the detected burst is a narrowband burst. Forinstance, in one implementation, to further certify that the detectedburst is a narrowband burst, it is determined whether the maximum binvalues in the narrowband FFT captures of the detected burst have smallvariations, which confirms that the narrowband FFT captures in thedetected burst contain data with the same or similar frequencies. Fromblock 255, the flow continues to block 260.

At block 260, it is determined whether enough data has been collected toclassify the received RF signal associated with the detected narrowbandburst as a certain type of narrowband signal. In some implementations,the received RF signal is classified when the classification unit 150detects a predetermined number of narrowband bursts and analyzes aplurality of parameters associated with the detected narrowband bursts.For instance, in one example, the received RF signal is classified whenthe classification unit 150 detects at least two or three narrowbandbursts and analyzes a plurality of parameters associated with thedetected narrowband bursts (e.g., burst width, frequency, etc.). In thisexample, if the detected narrowband burst is the first detectednarrowband burst, then classification unit 150 continues to acquire andanalyze FFT captures (i.e., process loops to block 205) to detect atleast one additional narrowband burst before moving forward with theclassification operation.

In some implementations, the received RF signal is classified when theclassification unit 150 detects and analyzes a predetermined number ofnarrowband bursts within a predetermined observation time window. Inthese implementations, if the classification unit 150 does not detectedthe predetermined number of narrowband bursts within the predeterminedobservation time window, the signal analysis operation restarts. Theclassification unit 150 may classify the narrowband burst as aBluetooth® signal, microwave oven leakage signal, frequency-hoppingspread-spectrum (FHSS) cordless phone signal, a baby monitor signal,etc., according to the classification criteria described below. Fromblock 260, the flow ends. It is noted that in other implementations theclassification unit 150 may perform the classification operation afterdetection of any suitable number of narrowband bursts. Furthermore, itis noted that in some implementations the classification unit 150 mayimplement different observation time windows and/or require a differentnumber of detected narrowband bursts for classifying differentnarrowband signal types (e.g., different requirements for Bluetooth®signal than for microwave oven leakage signal).

Examples of classification criteria that may be implemented by theclassification unit 150 for various types of narrowband signals whenanalyzing the parameters of the detected narrowband bursts are asfollows:

Microwave Oven Leakage Signals

-   -   Two consecutive bursts with length above 4 ms are observed;    -   Both burst are at the same frequency; and    -   The bursts have a gap of minimum 15 ms and maximum 60 ms in        between.        Alternatively:    -   Three consecutive bursts at the same frequency are observed;    -   The last burst's width is larger than 500 μs; and    -   The bursts are separated by at least 8 ms.        FHSS Cordless Phone Signals    -   The burst width is less than the slot duration of 1.25 ms;    -   The burst width is larger than the minimum burst width        threshold;    -   The time-gap between the last two consecutive bursts is within        the range of 5 ms;    -   The frequencies of the last two consecutive bursts are the same;        and    -   The third-to-last burst is at a different frequency than the        last two consecutive bursts.        Bluetooth® (A2DP) Signals    -   The burst width is above a Bluetooth® threshold and smaller than        a microwave threshold;    -   The last two bursts are at most 9.5 ms apart;    -   The last two bursts are at the same frequency; and    -   The third-to-last burst is at a different frequency than the        last two bursts.        Bluetooth® (Hands-Free) Signals    -   The last two bursts are at the same frequency;    -   The third-to-last burst is at a different frequency than the        last two bursts;    -   The last two bursts are at most 1 ms apart;    -   The last two bursts are at most 600 μs; and    -   The third burst is at least 3.5 ms away from the second burst.        Continuous Tone Signals    -   The last four bursts have frequency spread of zero, i.e., tone        shows up at the same FFT bin;    -   The last four bursts show up at the same frequency; and    -   The last burst lasts more than 800 μs.        Active Video Bridge Signals    -   The last burst is larger than 500 μs;    -   The gaps between bursts are smaller than 500 μs; and    -   The last three bursts are at most 4 MHz apart.

It should be understood that the depicted flowcharts are examples meantto aid in understanding embodiments and should not be used to limitembodiments or limit scope of the claims. Embodiments may performadditional operations, fewer operations, operations in a differentorder, operations in parallel, and some operations differently. Forinstance, in the description of FIG. 2, the various thresholdsconsidered during the FFT capture phase of the process and theclassification criteria considered during the classification phase ofthe process may be programmable. In one specific example, the amount oftime a signal is observed may be varied using the observation timewindow and/or the required minimum number of detected narrowband burstsmay be varied to help prevent false detections. Additionally, in someembodiments, a wideband detection classification technique based on theextraction and analysis of wideband bursts (block 230) may beimplemented, which may be similar to the narrowband technique describedabove. Furthermore, it is noted that in some embodiments the spectralscan data can be generated by other techniques besides FFT to performthe spectral analysis and classification operations.

In various implementations, after classification of a received RF signalas a certain narrowband (or wideband) signal type, the classificationunit 150 provides the classification information to the spectral scancontroller 155. The spectral scan controller 155 analyses theclassification information associated with the detected interferencesignal and determines what action to take in view of the interference.In one implementation, the spectral scan controller may change theoperational frequency of the WLAN device in order to avoid performanceloss. For example, the spectral scan controller may implement a channelselection algorithm that can use the total energy of the interference ina given frequency channel and/or the type of the interference and thefrequency occupancy pattern of the interference to change the channel ofthe WLAN device to avoid the interference.

In some cases, the spectral scan controller 155 may alert the user ofthe wireless system comprising the WLAN device and suggest methods toprevent or avoid the interference. For example, if a microwave ovenleakage signal is detected, the spectral scan controller 155 may suggestthat the user turn off the microwave oven. In some examples the spectralscan controller 155 may cause the spectral information and identifiedsources of interference to be displayed to the user via a graphical userinterface (GUI). For example, as the user is operating a laptop computercomprising the WLAN device, the user may be presented with a pop-upwindow providing the user with the interference information and asuggested course of action.

In some implementations, the spectral scan controller 155 may adjust thecoding rate, modulation packet length, and/or transmission power basedon the detected interference. For example, if the detected interferenceis periodic, the timing of when data is transmitted and received by theWLAN device can be adjusted to coincide with the times when theinterference is not present. In this example, a modulation scheme andtransfer rate can be used so that the data packets are kept relativelyshort to avoid the times when the interference is present.

In one embodiment, the spectral scan controller 155 enables and disablesthe FFT spectral scan operations for signal analysis. For example, thespectral scan controller 155 may provide an enable/disable signal to theFFT unit 110 to control when the FFT unit 110 provides thetime-frequency information and RSSI data to the classification unit 150.In some embodiments, the FFT spectral scan operations for signalanalysis may be implemented (e.g., in the receiver unit 110 of a WLANdevice) in at least two ways. In one example, the FFT spectral scanoperations can be implemented as an intrusive, high priority task. Inthis example, the spectral scan operations may have higher priority thandata traffic and therefore the data traffic may be suspended until thespectral scan operations have been completed. In another example, theFFT spectral scan operations can be implemented as a background processfor non-intrusive operation. In this example, the spectral scanoperations are low priority compared to data traffic and therefore thespectral scan operations co-exist with the data traffic. It is noted,however, that in some implementations the FFT spectral scan operationsmay be implemented as a low priority operation in some cases and a highpriority operation in other cases. For example, the default may be a lowpriority implementation, however, if certain interference is initiallydetected and/or if performance is being significantly affected, thespectral scan operations may be changed to high priority operations.

The FFT spectral scan operations can be initiated by a user or by anetwork administrator. The WLAN device can also automatically initiatespectral scan operations in a communication device (e.g., laptop oraccess point) based on whether a bit error rate (BER or a packet errorrate (PER) is above a certain threshold and/or if there is a suddenincrease in the BER/PER. As noted above, for the automatic mode, thespectral scan operations may be implemented as a low priority operation,high priority operation, or a combination of the two (i.e., low priorityin some instances and high priority in other instances).

FIG. 4 is a conceptual diagram of an example wireless communicationsystem. As illustrated, the communication system may include a pluralityof communication devices, such as a personal computer (PC) 401, mobilephone 402, a global positioning system (GPS) device 403, a WLAN accesspoint 404, a server 405 (e.g., accessible via the Internet), and alaptop 410, transmitting and receiving information via a wirelesscommunication network 450. In various implementations, the communicationdevices comprise a WLAN device 400 including a receiver unit (e.g., thereceiver unit 100 of FIG. 1) operable to receive WLAN signals and atransmitter unit 420 operable to transmit WLAN signals. The receiverunit 100 is further operable to implement some or all of the operationsand features described above with reference to FIGS. 1-3. For example,the WLAN access point 404 and laptop 410 utilize the receiver unit 100to implement the FFT spectral scan operations described above in FIGS.1-3 for processing and classifying received RF interference signals, andimplementing steps to reduce or eliminate the interference.

In some implementations, if a client device, e.g., the laptop 410, inthe communication system detects and classifies an RF interferencesignal, the client device may provide the interference type informationto one or more access points in the network, e.g., access point 404. Theinterference environment seen at an access point can be different fromthat of the client device. Therefore, the interference information maybe exchanged from a client device to an access point so that the accesspoint can understand the interference seen at the client device andutilize this information to make intelligent choices during operation,e.g., when selecting transmission channels. It is noted that in someimplementations the access points may also provide detected interferenceinformation to client devices for similar reasons.

FIG. 5 is an example flow diagram of a method for processing andclassifying narrowband interference signals in a wireless communicationsystem. In some embodiments, the client devices of the wirelesscommunication system (e.g., mobile phone 402, laptop 410, etc. shown inFIG. 4) may not have the capability (e.g., the analysis software) toperform the FFT spectral scan operations described above with referenceto FIGS. 1-3 for analysis and classification of RF interference signals.In these implementations, when the client device (e.g., laptop 410)receives an RF signal (block 510), the client device generates FFT data(e.g., time-frequency data and RSSI data) associated with the receivedRF signal and transmits the FFT data to an access point (e.g., accesspoint 404) via the wireless network 450 (block 520). The access pointperforms spectral scan operations based on the FFT data received fromthe client device to process and classify the RF signal as a certaininterference type, e.g., using the technique described above withreference to FIGS. 1-3 (block 530). The access point then transmits thedetermined interference type information to the client device via thenetwork 450 (block 540). In some examples, the access point may alsorecommend a course of action to the client device for reducing oreliminating the interference. In other examples, the client device mayhave the capability to determine one or more options for reducing oreliminating the interference based on the received interference typeinformation.

In some implementations, each access point (e.g., access point 404) in awireless communication system may transmit FFT data (e.g.,time-frequency data and RSSI data) generated at the access point and/orFFT data collected from various client devices in the network to acentral server (e.g., server 405). The central server performs signalclassification based on the received FFT data. Furthermore, the centralserver may perform channel selection (e.g., provided a recommended listof channels to the access points and the client devices) and make otherdecisions based on the classification results and the collectiveinformation received from the various access points. It is noted that insome implementations the client devices can provide the access point acompressed version of information such as whether the capture wasnarrowband/wideband and what is the index for peak power.

Embodiments may take the form of an entirely hardware embodiment, anentirely software embodiment (including firmware, resident software,micro-code, etc.) or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “circuit,”“module” or “system.” Furthermore, embodiments of the inventive subjectmatter may take the form of a computer program product embodied in anytangible medium of expression having computer usable program codeembodied in the medium. The described embodiments may be provided as acomputer program product, or software, that may include amachine-readable medium having stored thereon instructions, which may beused to program a computer system (or other electronic device(s)) toperform a process according to embodiments, whether presently describedor not, since every conceivable variation is not enumerated herein. Amachine readable medium includes any mechanism for storing ortransmitting information in a form (e.g., software, processingapplication) readable by a machine (e.g., a computer). Themachine-readable medium may include, but is not limited to, magneticstorage medium (e.g., floppy diskette); optical storage medium (e.g.,CD-ROM); magneto-optical storage medium; read only memory (ROM); randomaccess memory (RAM); erasable programmable memory (e.g., EPROM andEEPROM); flash memory; or other types of medium suitable for storingelectronic instructions. In addition, embodiments may be embodied in anelectrical, optical, acoustical or other form of propagated signal(e.g., carrier waves, infrared signals, digital signals, etc.), orwireline, wireless, or other communications medium.

Computer program code for carrying out operations of the embodiments maybe written in any combination of one or more programming languages,including an object oriented programming language such as Java,Smalltalk, C++ or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The program code may execute entirely on a user's computer,partly on the user's computer, as a stand-alone software package, partlyon the user's computer and partly on a remote computer or entirely onthe remote computer or server. In the latter scenario, the remotecomputer may be connected to the user's computer through any type ofnetwork, including a local area network (LAN), a personal area network(PAN), or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider).

FIG. 6 is an example block diagram of a wireless device. In oneimplementation, the wireless device may be a WLAN device (e.g., the WLANdevice 400 of FIG. 4). The WLAN device includes a processor unit 601(possibly including multiple processors, multiple cores, multiple nodes,and/or implementing multi-threading, etc.). The WLAN device includesmemory 607. The memory 607 may be system memory (e.g., one or more ofcache, SRAM, DRAM, zero capacitor RAM, Twin Transistor RAM, eDRAM, EDORAM, DDR RAM, EEPROM, NRAM, RRAM, SONOS, PRAM, etc.) or any one or moreof the above already described possible realizations of machine-readablemedia. The WLAN device also includes a bus 603 (e.g., PCI, ISA,PCI-Express, HyperTransport®, InfiniBand®, NuBus, etc.), and networkinterfaces 605 that include at least one wireless network interface(e.g., a WLAN interface, a Bluetooth® interface, a WiMAX interface, aZigBee® interface, a Wireless USB interface, etc.). The WLAN device alsoincludes a classification unit 615 that implements the functionalitiesdescribed above with reference to FIGS. 1-5. Any one of the abovedescribed functionalities may be partially (or entirely) implemented inhardware and/or on the processing unit 601. For example, thefunctionality may be implemented with an application specific integratedcircuit, in logic implemented in the processing unit 601, in aco-processor on a peripheral device or card, etc. Further, realizationsmay include fewer or additional components not illustrated in FIG. 6(e.g., additional network interfaces, peripheral devices, etc.). Theprocessor unit 601 and the network interfaces 605 are coupled to the bus603. Although illustrated as being coupled to the bus 603, the memory607 may be coupled to the processor unit 601.

While the embodiments are described with reference to variousimplementations and exploitations, it will be understood that theseembodiments are illustrative and that the scope of the inventive subjectmatter is not limited to them. In general, the RF signal detection andclassification techniques as described herein may be implemented withfacilities consistent with any hardware system or systems. Manyvariations, modifications, additions, and improvements are possible.

Plural instances may be provided for components, operations orstructures described herein as a single instance. Finally, boundariesbetween various components, operations and data stores are somewhatarbitrary, and particular operations are illustrated in the context ofspecific illustrative configurations. Other allocations of functionalityare envisioned and may fall within the scope of the inventive subjectmatter. In general, structures and functionality presented as separatecomponents in the exemplary configurations may be implemented as acombined structure or component. Similarly, structures and functionalitypresented as a single component may be implemented as separatecomponents. These and other variations, modifications, additions, andimprovements may fall within the scope of the inventive subject matter.

1. A wireless local area network (WLAN) device comprising: a transmitterunit operable to transmit RF signals via a network; and a receiver unitcoupled to the transmitter unit and operable to receive RF signals viathe network, wherein the receiver unit comprises: a spectral scan unitoperable to generate a plurality of spectral data captures comprisingtime-frequency data and receive signal strength indicator (RSSI) dataassociated with an RF signal received at the WLAN device; and aclassification unit operable to process the time-frequency and RSSI dataof each of the plurality of spectral data captures to determine whethereach spectral data capture is a narrowband signal capture; wherein, ifthe plurality of spectral data captures are narrowband signal captures,the classification unit is operable to determine a narrowband signaltype associated with the received RF signal based, at least in part, ona plurality of parameters associated with the narrowband signalcaptures; wherein, in a first mode of operation, the WLAN device isoperable to decode the received RF signals for WLAN traffic and transmitWLAN traffic, and in a second mode of operation, the WLAN device isoperable to perform spectral analysis and classification operations onthe received RF signals.
 2. The WLAN device of claim 1, wherein, foreach spectral data capture, the classification unit is operable todetermine whether the RSSI data associated with the spectral datacapture is greater than a predetermined RSSI threshold, wherein, if theRSSI data is greater than the predetermined RSSI threshold, theclassification unit is operable process the time-frequency dataassociated with the spectral data capture.
 3. The WLAN device of claim2, wherein, for each spectral data capture, the time-frequency datacomprises a plurality of frequency bins associated with the spectraldata capture, wherein the classification unit is operable to determine abin count of the plurality of frequency bins that are greater than apredetermined decibel threshold, and wherein the classification unit isfurther operable to compare the bin count of the frequency bins that aregreater than the predetermined decibel threshold to a predetermined bincount threshold to determine whether the spectral data capture is anarrowband spectral data capture.
 4. The WLAN device of claim 3,wherein, for each spectral data capture, if the bin count is less thanthe predetermined bin count threshold, the classification unit isoperable to classify the spectral data capture as a narrowband spectraldata capture.
 5. The WLAN device of claim 3, wherein, for each spectraldata capture, if the bin count is greater than the predetermined bincount threshold, the classification unit is operable to classify thespectral data capture as a wideband spectral data capture.
 6. The WLANdevice of claim 4, wherein the classification unit is operable todetermine whether the narrowband spectral data capture is a firstnarrowband spectral data capture in a sequence of narrowband spectraldata captures, wherein, if the narrowband spectral data capture is thefirst narrowband spectral data capture, the classification unit isoperable to record a narrowband burst start time associated with thefirst narrowband spectral data capture, wherein, if the narrowbandspectral data capture is not the first narrowband spectral data capturein the sequence of narrowband spectral data captures, the classificationunit is operable to record a plurality of parameters associated with thenarrowband spectral data capture.
 7. The WLAN device of claim 6, whereinthe classification unit is operable to determine whether a narrowbandburst associated with the sequence of narrowband spectral data captureshas been detected based, at least in part, on a number of consecutivenarrowband spectral data captures and a duration between a firstnarrowband spectral data capture and a last narrowband spectral datacapture, wherein, if the number of consecutive narrowband spectral datacaptures is greater than a predetermined count threshold and theduration between the first narrowband spectral data capture and the lastnarrowband spectral data capture is greater than a predeterminedduration threshold, the classification unit is operable to determinethat a narrowband burst has been detected.
 8. The WLAN device of claim7, wherein, if the classification unit determines that a narrowbandburst has been detected, the classification unit is operable to record anarrowband burst stop time, and calculate a narrowband burst width basedon a difference between the recorded narrowband burst stop time andnarrowband burst start time, wherein the classification unit is furtheroperable to classify the received RF signal as a certain type ofnarrowband signal based, at least in part, on a plurality of parametersassociated with the detected narrowband burst and one or more additionaldetected narrowband bursts associated with the received RF signal. 9.The WLAN device of claim 8, wherein the classification unit is operableto monitor whether an amount of time that elapses between narrowbandspectral data captures is greater than a predetermined timestepthreshold, wherein, if the amount of time that elapses betweennarrowband spectral data captures is greater than the predeterminedtimestep threshold, the classification unit is operable to record anarrowband burst stop time, and calculate a narrowband burst width basedon a difference between the recorded narrowband burst stop time andnarrowband burst start time.
 10. The WLAN device of claim 1, wherein thespectral scan unit is a Fast Fourier Transform (FFT) unit operable togenerate a plurality of FFT captures comprising time-frequency data andRSSI data associated with the RF signal received at the WLAN device. 11.The WLAN device of claim 1, wherein the receiver unit further comprisesone or more signal processing units operable to process WLAN traffic,wherein, in the first mode of operation, the spectral scan unit isoperable to provide the spectral data captures associated with receivedRF signals to the signal processing units to decode the received RFsignals for WLAN traffic, wherein, in the second mode of operation, thespectral scan unit is operable to provide the spectral data capturesassociated with the received RF signals to the classification unit forperforming spectral analysis and classification operations on thereceived RF signals.
 12. The WLAN device of claim 1, further comprisinga spectral scan controller, wherein in response to identifying aninterference signal by classifying a received RF signal as a narrowbandsignal type, the classification unit is operable to provideclassification information about the interference signal to the spectralscan controller, wherein the spectral scan controller is operable tochange an operational frequency of the WLAN device to avoid theinterference signal.
 13. A method for processing and classifying an RFsignal received at a wireless local area network (WLAN) device, themethod comprising: generating a plurality of Fast Fourier Transform(FFT) captures comprising time-frequency data and receive signalstrength indicator (RSSI) data associated with the RF signal received atthe WLAN device; processing the time-frequency and RSSI data of each ofthe plurality of FFT captures to determine whether each FFT capture is anarrowband signal capture; and if the plurality of FFT captures arenarrowband signal captures, determining a narrowband signal typeassociated with the received RF signal based, at least in part, on aplurality of parameters associated with the narrowband signal captures.14. The method of claim 13, wherein said processing the time-frequencyand RSSI data of each of the plurality of FFT captures to determinewhether each FFT capture is a narrowband signal capture comprises: foreach FFT capture, determining whether the RSSI data associated with theFFT capture is greater than a predetermined RSSI threshold; and for eachFFT capture, if the RSSI data is greater than the predetermined RSSIthreshold, determining a bin count of a plurality of frequency binsassociated with the FFT capture that are greater than a predetermineddecibel threshold, and comparing the bin count of the frequency binsthat are greater than the predetermined decibel threshold to apredetermined bin count threshold to determine whether the FFT captureis a narrowband FFT capture.
 15. The method of claim 14, wherein, foreach FFT capture, if the bin count is less than the predetermined bincount threshold, classifying the FFT capture as a narrowband FFTcapture.
 16. The method of claim 14, wherein, for each FFT capture, ifthe bin count is greater than the predetermined bin count threshold,classifying the FFT capture as a wideband FFT capture.
 17. The method ofclaim 15, wherein said determining a narrowband signal type associatedwith the received RF signal comprises determining whether the narrowbandFFT capture is a first narrowband FFT capture in a sequence ofnarrowband FFT captures, wherein, if the narrowband FFT capture is thefirst narrowband FFT capture, recording a narrowband burst start timeassociated with the first narrowband FFT capture, wherein, if thenarrowband FFT capture is not the first narrowband FFT capture in thesequence of narrowband FFT captures, recording a plurality of parametersassociated with the narrowband FFT capture.
 18. The method of claim 17,wherein said determining a narrowband signal type associated with thereceived RF signal comprises determining whether a narrowband burstassociated with the sequence of narrowband FFT captures has beendetected based, at least in part, on a number of consecutive narrowbandFFT captures and a duration between a first narrowband FFT capture and alast narrowband FFT capture, wherein, if the number of consecutivenarrowband FFT captures is greater than a predetermined count thresholdand the duration between the first narrowband FFT capture and the lastnarrowband FFT capture is greater than a predetermined durationthreshold, determining that a narrowband burst has been detected. 19.The method of claim 18, wherein said determining a narrowband signaltype associated with the received RF signal comprises, if a narrowbandburst has been detected, recording a narrowband burst stop time, andcalculating a narrowband burst width based on a difference between therecorded narrowband burst stop time and narrowband burst start time, andclassifying the received RF signal as a certain type of narrowbandsignal based, at least in part, on a plurality of parameters associatedwith the detected narrowband burst and one or more additional detectednarrowband bursts associated with the received RF signal.
 20. The methodof claim 19, further comprising monitoring whether an amount of timethat elapses between narrowband FFT captures is greater than apredetermined timestep threshold, wherein, if the amount of time thatelapses between narrowband FFT captures is greater than thepredetermined timestep threshold, recording a narrowband burst stoptime, and calculating a narrowband burst width based on a differencebetween the recorded narrowband burst stop time and narrowband burststart time.
 21. The method of claim 13, further comprising, when theWLAN device is processing WLAN traffic, utilizing the FFT captures toprocess the WLAN traffic, and when the WLAN device is not processingWLAN traffic, utilizing the FFT captures associated with a received RFsignal to process and classify the received RF signal.
 22. A wirelesscommunication system comprising: a wireless client device operable toreceive an RF signal, wherein the wireless client device comprises aFast Fourier Transform (FFT) unit operable to generate a plurality ofFFT captures comprising time-frequency data and receive signal strengthindicator (RSSI) data associated with the received RF signal; and awireless access point device operable to communicate with the wirelessclient device via a wireless network, wherein the wireless access pointdevice is operable to receive the plurality of FFT captures from thewireless client device, wherein the wireless access point devicecomprises: a classification unit operable to process the time-frequencyand RSSI data of each of the plurality of FFT captures to determinewhether each FFT capture is a narrowband signal capture; wherein, if theplurality of FFT captures are narrowband signal captures, theclassification unit is operable to determine a narrowband signal typeassociated with the RF signal received at the wireless client devicebased, at least in part, on a plurality of parameters associated withthe narrowband signal captures.
 23. The wireless communication system ofclaim 22, wherein, for each FFT capture, the classification unit isoperable to determine whether the RSSI data associated with the FFTcapture is greater than a predetermined RSSI threshold, wherein, if theRSSI data is greater than the predetermined RSSI threshold, theclassification unit is operable process the time-frequency dataassociated with the FFT capture.
 24. The wireless communication systemof claim 23, wherein, for each FFT capture, the time-frequency datacomprises a plurality of frequency bins associated with the FFT capture,wherein the classification unit is operable to determine a bin count ofthe plurality of frequency bins that are greater than a predetermineddecibel threshold, and wherein the classification unit is furtheroperable to compare the bin count of the frequency bins that are greaterthan the predetermined decibel threshold to a predetermined bin countthreshold to determine whether the FFT capture is a narrowband FFTcapture.
 25. The wireless communication system of claim 24, wherein, foreach FFT capture, if the bin count is less than the predetermined bincount threshold, the classification unit is operable to classify the FFTcapture as a narrowband FFT capture.
 26. The wireless communicationsystem of claim 25, wherein the classification unit is operable todetect a plurality of narrowband bursts associated with a plurality ofnarrowband FFT captures, wherein the classification unit is operable todetermine a narrowband signal type associated with the RF signalreceived at the wireless client device based, at least in part, on aplurality of parameters associated with the plurality of narrowbandbursts associated with the plurality of narrowband FFT captures.
 27. Thewireless communication system of claim 26, wherein, after determiningthe narrowband signal type, the wireless access point device is operableto provide classification data indicating the narrowband signal type tothe wireless client device.
 28. One or more machine-readable mediahaving stored therein a program product, which when executed a set ofone or more processor units causes the set of one or more processorunits to perform operations that comprise: generating a plurality ofFast Fourier Transform (FFT) captures comprising time-frequency data andreceive signal strength indicator (RSSI) data associated with a RFsignal received at a WLAN device; processing the time-frequency and RSSIdata of each of the plurality of FFT captures to determine whether eachFFT capture is a narrowband signal capture; and if the plurality of FFTcaptures are narrowband signal captures, determining a narrowband signaltype associated with the received RF signal based, at least in part, ona plurality of parameters associated with the narrowband signalcaptures.
 29. The machine-readable media of claim 28, wherein theprogram product when executed causes the set of one or more processorunits to perform operations that comprise: for each FFT capture,determining whether the RSSI data associated with the FFT capture isgreater than a predetermined RSSI threshold; and for each FFT capture,if the RSSI data is greater than the predetermined RSSI threshold,determining a bin count of a plurality of frequency bins associated withthe FFT capture that are greater than a predetermined decibel threshold,and comparing the bin count of the frequency bins that are greater thanthe predetermined decibel threshold to a predetermined bin countthreshold to determine whether the FFT capture is a narrowband FFTcapture.
 30. The machine-readable media of claim 29, wherein the programproduct when executed causes the set of one or more processor units toperform operations that comprise, for each FFT capture, if the bin countis less than the predetermined bin count threshold, classifying the FFTcapture as a narrowband FFT capture.